The pose estimation from a two-dimensional image is a hot topic in the field of computer. The pose estimation technology may be applied in many fields such as human-computer interaction, video monitoring and analysis of digital information.
In principal, the pose estimation technology may be classified as the pose estimation method based on model and the pose estimation method based on learning.
In the pose estimation method based on model, a body model constituting by body parts is created and the pose estimation is implemented by searching and matching the closest pose in a searching space with the body model. For example, J. M. Rehg and T. Kanade proposed a pose estimation method based on model in “Model-based tracking of selfoccluding articulated objects” (see, ICCV, page 612-617, 1995). Mun Wai Lee and Cohen proposed a pose estimation method based on model in “A model-based approach for estimating human 3D poses in static images” (see, IEEE TPAMI 28(6):905-916). The above-mentioned documents are incorporated herein by reference in their entirety.
In the pose estimation technology based on learning, the pose of the object is derived from image features of the object and the most used image feature is the contour information of the object. In order to obtain the contour information, the motion analysis method, the background modeling method or the combination thereof is used. For example, A. Agarwal and B. Triggs proposed a method for deriving a 3D pose based on the contour information of the objection in “3d human pose from silhouettes by relevance vector regression” (see, CVPR, vol 2:pp. 882-888, 2004) in which the contour information is obtained with the motion analysis method. R. Rosales and S. Sclaroff proposed a method for deriving a 3D pose based on the contour information of the objection in “Learning body pose via specialized maps” (see, NIPS, 2002) in which the contour information is obtained with the background modeling method. The above-mentioned documents are incorporated herein by reference in their entirety.
In current pose estimation technologies, the estimation for the body is a key part of the pose estimation.